Python for Scientific Computing (for Windows 64-bit)
Overview
Details
This add-on is available for Linux (64-bit), Windows (64-bit) and Mac. Make sure you install the appropriate one for your Splunk deployment.
Requirements
Python for Scientific Computing (PSC) Add-on is a requirement for some of the applications in Splunkbase such as:
- Machine Learning Toolkit (MLTK Beta and Advisory apps as well)
- Splunk Security Essentials for Fraud Detection
- DGA App for Splunk
- Suricata app for splunk
Splunk’s users have also used PSC and/or MLTK in combination with other applications such as:
- Splunk Security Essentials
- App for AWS Billing
- Splunk ITSI Module for Application Performance Monitoring etc.
Download
Please download and install the appropriate version here:
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Mac Intel: https://splunkbase.splunk.com/app/2881/
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Mac Apple Silicon: https://splunkbase.splunk.com/app/6785/
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Linux 64-bit: https://splunkbase.splunk.com/app/2882/
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Linux 32-bit: https://splunkbase.splunk.com/app/2884/
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Windows 64-bit: https://splunkbase.splunk.com/app/2883/
Installation
To install an app within Splunk Enterprise:
- Log into Splunk Enterprise.
- Next to the Apps menu, click the Manage Apps icon.
- Click Install app from file.
- In the Upload app dialog box, click Choose File.
- Locate the .tar.gz or .tar file you just downloaded, then click Open or Choose.
- Click Upload.
Release Notes
Version 4.2.0
This release of Python for Scientific Computing (PSC) introduces a number of new libraries such as: * Python libraries that monitor access and file transfers to the AWS S3 buckets: boto3, botocore, and s3transfer. * Scalable library for matrix profiling and time series data mining tasks: stumpy * Libraries with Dynamic Time Warping (DTW) algorithms for optimal alignment with O(N) time and memory complexity: fastdw * New versions of math libraries for intel and intel compatible processors. * Packages that implement pythonic file system spec and HTTP thread-safe connection pooling: fsspec, urllib3, and requests * This version includes the utility packages: chardet, future, idna, and six.
Version 3.1.0
This release of Python for Scientific Computing (PSC) introduces a new ONNX library for MLTK's onnx inference feature. It also fixes an issue preventing PSC from being upgraded when a search using PSC is running.
This build is the same with the 4.1.0 build without pytorch and transformers
Version 4.1.0
This release of Python for Scientific Computing (PSC) introduces a new ONNX library for MLTK's onnx inference feature. It also fixes an issue preventing PSC from being upgraded when a search using PSC is running.
Version 4.0.0
This release of Python for Scientific Computing (PSC) introduces a number of new scientific and machine learning libraries, such as: Transformers, PyTorch and ONNX Runtime.
Version 3.0.2
This release of Python for Scientific Computing (PSC) does not bring any major updates, only bug fixes.
Everything remains the same as in PSC 3.0.1 except MLTK 5.3.1 will be compatible with PSC 3.0.0 and above which includes PSC 3.0.1 and PSC 3.0.2.
Note that the streaming_apply feature has been deprecated and PSC should no longer be installed on indexers.
Version 3.0.1
This release of the Python for Scientific Computing (PSC) add-on is limited to configuration updates for deployment on Splunk Cloud Platform.
MLTK versions 5.2.2 or lower are not compatible with this version of PSC. If you are upgrading to this version of PSC, you must also upgrade your installation of MLTK to version 5.3.0 or higher.
Version 3.0.0
This release of Python for Scientific Computing (PSC) brings updates to several libraries in the package. In particular, Numpy, Scipy, scikit-learn, Statsmodels, and Networkx are upgraded to their latest available versions.
MLTK versions earlier than 5.3.0 are not compatible with this version of PSC. Therefore, if you are upgrading to this version of PSC, you must also upgrade your installation of MLTK to version 5.3.0 or later.